Psychonomic Bulletin & Review

, Volume 21, Issue 3, pp 689–695 | Cite as

Visual similarity is stronger than semantic similarity in guiding visual search for numbers

  • Hayward J. Godwin
  • Michael C. Hout
  • Tamaryn Menneer
Brief Report

Abstract

Using a visual search task, we explored how behavior is influenced by both visual and semantic information. We recorded participants’ eye movements as they searched for a single target number in a search array of single-digit numbers (0–9). We examined the probability of fixating the various distractors as a function of two key dimensions: the visual similarity between the target and each distractor, and the semantic similarity (i.e., the numerical distance) between the target and each distractor. Visual similarity estimates were obtained using multidimensional scaling based on the independent observer similarity ratings. A linear mixed-effects model demonstrated that both visual and semantic similarity influenced the probability that distractors would be fixated. However, the visual similarity effect was substantially larger than the semantic similarity effect. We close by discussing the potential value of using this novel methodological approach and the implications for both simple and complex visual search displays.

Keywords

Eye movements Visual search 

Supplementary material

13423_2013_547_MOESM1_ESM.doc (80 kb)
Supplementary Table S1Two-dimensional MDS coordinates for each of the ten digit stimuli (DOC 80 kb)
13423_2013_547_MOESM2_ESM.doc (97 kb)
Supplementary Table S2MDS distances (in arbitrary units) between each pair of digits. (DOC 97 kb)
13423_2013_547_MOESM3_ESM.doc (184 kb)
Supplementary Fig. S1The plot on the left shows the data from Shepard et al. (1975). There, participants were shown Arabic numerals, two at a time, and were asked to rate the similarity of each pair (using a slide marker that comprised a 21-point scale). The plot on the left shows the present data, for which participants rated the similarity of the numbers using the spatial arrangement method (Goldstone, 1994). The x-axis coordinates for our data were reversed, to match the overall orientation of the Shepard data (the coordinate axes are arbitrary). In both instances, it is clear that the two dimensions used to rate similarity were curvature and the extent to which the numbers comprised open or closed spaces. (DOC 183 kb)

References

  1. Alexander, R. G., & Zelinsky, G. J. (2011). Visual similarity effects in categorical search. Journal of Vision, 11(8), 9. doi:10.1167/11.8.9. 1–15.PubMedCrossRefGoogle Scholar
  2. Bates, D., Maechler, M., & Bolker, B. (2012). lme4: Linear mixed-effects models using S4 classes [Software]. Retrieved from http://CRAN.R-project.org/package=lme4
  3. Becker, S. I. (2011). Determinants of dwell time in visual search: Similarity of perceptual difficulty? PLoS One, 6, e17740. doi:10.1371/journal.pone.0017740 PubMedCentralPubMedCrossRefGoogle Scholar
  4. Busing, F. M. T. A., Commandeur, J. J. F., Heiser, W. J., Bandilla, W., & Faulbaum, F. (1997). PROXSCAL: A multidimensional scaling program for individual differences scaling with constraints. Advances in Statistical Software, 6, 67–73.Google Scholar
  5. Chun, M. M., & Wolfe, J. M. (1996). Just say no: How are visual searches terminated when there is no target present? Cognitive Psychology, 30, 39–78. doi:10.1006/cogp.1996.0002 PubMedCrossRefGoogle Scholar
  6. Cohen, D. J., & Blanc-Goldhammer, D. (2012). Numerical bias in bounded and unbounded number line tasks. Psychonomic Bulletin & Review, 18, 331–338. doi:10.3758/s13423-011-0059-z CrossRefGoogle Scholar
  7. Goldstone, R. L. (1994). An efficient method for obtaining similarity data. Behavior Research Methods, Instruments, & Computers, 26, 381–386. doi:10.3758/BF03204653 CrossRefGoogle Scholar
  8. Henderson, J. M., Malcolm, G. L., & Schandl, C. (2009). Searching in the dark: Cognitive relevance drives attention in real-world scenes. Psychonomic Bulletin & Review, 16, 850–856. doi:10.3758/PBR.16.5.850 CrossRefGoogle Scholar
  9. Hout, M. C., Goldinger, S. D., & Ferguson, R. W. (2013). The versatility of SpAM: A fast, efficient spatial method of data collection for multidimensional scaling. Journal of Experimental Psychology: General, 142, 256–281. doi:10.1037/a0028860 CrossRefGoogle Scholar
  10. Hout, M. C., Papesh, M. H., & Goldinger, S. D. (2012). Multidimensional scaling. Wiley Interdisciplinary Reviews: Cognitive Science, 4, 93–103. doi:10.1002/wcs.1203 PubMedCentralPubMedGoogle Scholar
  11. Jaworska, N., & Chupetlovska-Anastasova, A. (2009). A review of multidimensional scaling (MDS) and its utility in various psychological domains. Tutorial in Quantitative Methods for Psychology, 5, 1–10.Google Scholar
  12. Luria, S. M., & Strauss, M. S. (1975). Eye movements during search for coded and uncoded targets. Perception & Psychophysics, 17, 303–308. doi:10.3758/BF03203215 CrossRefGoogle Scholar
  13. Oliva, A., & Torralba, A. (2007). The role of context in object recognition. Trends in Cognitive Sciences, 11, 520–527. doi:10.1016/j.tics.2007.09.009 PubMedCrossRefGoogle Scholar
  14. Rayner, K., & Fisher, D. L. (1987). Letter processing during eye fixations in visual search. Perception & Psychophysics, 42, 87–100. doi:10.3758/BF03211517 CrossRefGoogle Scholar
  15. Schwarz, W., & Eiselt, A.-K. (2012). Numerical distance effects in visual search. Attention, Perception, & Psychophysics, 74, 1098–1103. doi:10.3758/s13414-012-0342-8 CrossRefGoogle Scholar
  16. Shepard, R. N., Kilpatric, D. W., & Cunningham, J. P. (1975). The internal representation of numbers. Cognitive Psychology, 7, 82–138. doi:10.1016/0010-0285(75)90006-7 CrossRefGoogle Scholar
  17. Stroud, M. J., Menneer, T., Cave, K. R., & Donnelly, N. (2012). Using the dual-target cost to explore the nature of search target representations. Journal of Experimental Psychology: Human Perception and Performance, 38, 113–122. doi:10.1037/a0025887 PubMedGoogle Scholar
  18. Williams, L. G. (1967). The effects of target specification on objects fixated during visual search. Acta Psychologica, 27, 355–360. doi:10.1016/0001-6918(67)90080-7 PubMedCrossRefGoogle Scholar
  19. Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989). Guided search: An alternative to the feature integration model for visual search. Journal of Experimental Psychology: Human Perception and Performance, 15, 419–433. doi:10.1037/0096-1523.15.3.419 PubMedGoogle Scholar
  20. Wolfe, J. M., & Horowitz, T. S. (2004). What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience, 5, 495–501. doi:10.1038/nrn1411 PubMedCrossRefGoogle Scholar
  21. Wolfe, J. M., Võ, M. L.-H., Evans, K. K., & Greene, M. R. (2011). Visual search in scenes involves selective and nonselective pathways. Trends in Cognitive Sciences, 15, 77–84. doi:10.1016/j.tics.2010.12.001 PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  • Hayward J. Godwin
    • 1
  • Michael C. Hout
    • 2
  • Tamaryn Menneer
    • 1
  1. 1.School of PsychologyUniversity of SouthamptonSouthamptonUK
  2. 2.New Mexico State UniversityLas CrucesUSA

Personalised recommendations